Spatial conservation plans are typically based upon uncertain inputs and may benefit from additional data to inform them.However, the toolset of spatial prioritization does not yet contain a method for assessing the value of new information to a spatial conservation plan. If the value of information were to be calculated, then conservation plans would more effective, benefiting from a more optimal amount of information. Here, for the first time we demonstrate how a formal value of information analysis can be applied to a spatial conservation plan. We show how a value of information analysis can be combined with traditional conservation planning tools to map species distributions and optimize a reserve network to protect them. We incorporate uncertainty into conservation planning with Monte Carlo sampling of the planning inputs and then test the effects of uncertainty reduction to calculate the value of additional information to a conservation plan. The impact of optimally incorporating additional information into conservation plans, will be more effective plans where additional information is beneficial, and avoiding the loss of resources to uneccessary information gathering where new data has no benefit to the fundemental objectives of the plan.
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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